Transcription of An Intuitive Tutorial to Gaussian Processes Regression
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An Intuitive Tutorial to Gaussian Processes Regression
An Intuitive Tutorial to Gaussian Processes Regression [ ] 2 Feb 2021. Jie Wang Ingenuity Labs Research Institute February 3, 2021. Offroad Robotics c/o Ingenuity Labs Research Institute Queen's University Kingston, ON K7L 3N6 Canada Abstract This Tutorial aims to provide an Intuitive understanding of the Gaussian Processes Regression . Gaussian Processes Regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherently uncertainty measures over predictions. The basic concepts that a Gaussian process is built on, including multivariate normal distribution, kernels, non-parametric models, joint and conditional probability were explained first. Next, the GPR was described concisely together with an implementation of a standard GPR algorithm.
Gaus-sian processes model is a supervised learning method developed by computer sci-ence and statistics communities. Researchers with engineering backgrounds often find it is difficult to gain a clear understanding of it. To understand GPR, even only the basics needs to have knowledge of multivariate normal distribution, kernels,
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